Quality signals
Reading AI output like a pro: the signals that predict trouble, the ones that don't, and the 30-second triage that catches most errors.
By now you can get good output. This module teaches the complementary skill that actually protects your reputation: judging it. Start with the signals — which surface features of a response predict problems, and which are noise.
Signals that mean nothing (stop being reassured by them)
- Confidence. The model produces exactly one tone — assured — for right and wrong answers alike.
- Fluency and formatting. Clean bullets and headers are the house style, not evidence of care.
- Specificity of numbers. '17.3%' feels researched; it's rendered with the same conviction whether computed, recalled, or invented.
- Length. Longer ≠ more thorough. Often it's the same content padded with restatement.
Signals that actually predict trouble
- Suspicious convenience. Every cited figure rounds nicely, every example fits perfectly, all evidence points one way. Reality is lumpier than that.
- Uniform texture. In real expertise, some parts are detailed and some thin. AI bluffing has an even, smooth coverage everywhere — including where detail should cluster.
- Unfalsifiable filler. 'Many experts agree', 'studies show', 'it's widely recognized' — claims with no handle to check are the model papering over a gap.
- Drift from your constraints. You said 'under 150 words, no vendor names' and got 300 words with a vendor named. If it dropped visible constraints, assume it also dropped invisible facts.
- Boundary-zone content. The moment output crosses from transforming your input to asserting facts you didn't provide, the error rate jumps. Learn to notice the crossing.
The 30-second triage
- 1Scan for claims vs. transformations. Highlight (mentally or literally) every statement that didn't come from your input.
- 2Rate the stakes. Internal brainstorm → light check. Going to a client or a decision → full Module-5 treatment.
- 3Check constraint compliance. Length, tone, exclusions — 5 seconds, and a proxy for overall care.
- 4Spot-check one verifiable detail. Pick the claim that would hurt most if wrong. Verify just that one. Its accuracy is your sample of the rest.
Review your previous answer as a skeptical fact-checker. Categorize every substantive statement as: (a) restatement of information I gave you, (b) widely-established knowledge, or (c) a specific claim that should be verified before anyone relies on it. List all the (c) items.
This is the triage automated. The (c) list is your verification to-do — and it's usually far shorter than the full text, which is why checking is cheaper than it feels.